store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_1039002', 'seed': 1039002, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[47860, 23464, 15813, 48044, 5751, 33233, 7322, 31989, 36795, 27695, 17006, 49503, 18331, 31104, 15801, 16795, 58615, 38588, 20462, 50124]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.5656, 'nll': 3.041668009757996}, 'chosen_samples': [36096, 36781, 21792, 12202, 38898, 58895, 42297, 6755, 31094, 42681], 'chosen_samples_score': ['1.1551576', '1.1916194', '1.2040048', '1.1721675', '1.1775452', '1.1811972', '1.1852183', '1.1737723', '1.1993576', '1.1968935']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6772, 'nll': 1.9763166666030885}, 'chosen_samples': [9937, 25521, 43538, 35138, 37266, 27197, 30162, 16888, 30390, 42756], 'chosen_samples_score': ['1.0540328', '1.0559978', '1.0622339', '1.0845337', '1.0659385', '1.133292', '1.1240594', '1.0700666', '1.0653491', '1.1290181']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7042, 'nll': 1.7747600555419922}, 'chosen_samples': [52896, 14277, 34902, 22954, 31634, 39942, 57442, 29904, 37192, 25341], 'chosen_samples_score': ['1.030239', '1.0335741', '1.0477774', '1.0360548', '1.0551429', '1.1042926', '1.0770888', '1.0781789', '1.0811955', '1.1323426']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7843, 'nll': 1.265704047679901}, 'chosen_samples': [17010, 36398, 10231, 10203, 8892, 40130, 32538, 19829, 29483, 32381], 'chosen_samples_score': ['1.0387803', '1.0874541', '1.0859021', '1.0735624', '1.0423964', '1.049715', '1.0894994', '1.0911388', '1.0891652', '1.0567816']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8463, 'nll': 1.0251734912395478}, 'chosen_samples': [27669, 2143, 54646, 14707, 12196, 52154, 29246, 47475, 13845, 47115], 'chosen_samples_score': ['1.0695598', '1.0700521', '1.0970688', '1.2508059', '1.3185807', '1.1261947', '1.16694', '1.1013805', '1.1123209', '1.1182053']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8463, 'nll': 0.9277821242809295}, 'chosen_samples': [1121, 37293, 29390, 10522, 37758, 22529, 9318, 32276, 9687, 58560], 'chosen_samples_score': ['1.0411575', '1.0448427', '1.0452117', '1.0947907', '1.0678495', '1.0944362', '1.0536327', '1.1104187', '1.0605863', '1.1129075']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8713, 'nll': 0.8203289091587067}, 'chosen_samples': [16488, 11208, 11693, 7891, 5647, 38577, 25646, 20119, 18598, 37391], 'chosen_samples_score': ['0.99854517', '1.0032613', '0.999469', '1.0018241', '1.0170251', '1.029655', '1.0326502', '1.050708', '1.0473301', '1.0143892']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.87, 'nll': 0.8597987532615662}, 'chosen_samples': [4822, 26733, 1814, 16219, 19590, 5684, 10265, 21287, 38372, 55555], 'chosen_samples_score': ['1.0522332', '1.0607799', '1.0576391', '1.0579171', '1.061379', '1.0593988', '1.0664153', '1.136939', '1.133933', '1.1361468']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8872, 'nll': 0.7667501866817474}, 'chosen_samples': [12986, 13753, 37584, 29725, 47511, 8584, 38256, 43424, 31124, 59380], 'chosen_samples_score': ['1.0874814', '1.097107', '1.1147671', '1.1968011', '1.1395192', '1.1902845', '1.287368', '1.1367506', '1.1359396', '1.1868051']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9, 'nll': 0.650455167889595}, 'chosen_samples': [49567, 3668, 52771, 15848, 17961, 8116, 32434, 22883, 5710, 24426], 'chosen_samples_score': ['0.92433923', '0.9410074', '0.9279709', '0.9441373', '0.94599605', '0.9640368', '1.040942', '0.95015407', '0.9476335', '0.9464626']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.913, 'nll': 0.6075035959482193}, 'chosen_samples': [38920, 41229, 42078, 40457, 36421, 30626, 26444, 39668, 44445, 49525], 'chosen_samples_score': ['1.1097459', '1.1193669', '1.1544662', '1.1245742', '1.1841681', '1.1785148', '1.2147676', '1.148833', '1.1487923', '1.171733']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9154, 'nll': 0.5950031489133835}, 'chosen_samples': [54490, 22677, 52686, 36409, 17393, 21421, 28014, 3030, 32391, 4646], 'chosen_samples_score': ['1.0479125', '1.0524521', '1.0567838', '1.0586336', '1.0717272', '1.0775703', '1.082843', '1.0870721', '1.0895085', '1.0846176']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9262, 'nll': 0.5907310158014297}, 'chosen_samples': [5013, 2000, 21426, 17756, 6942, 23642, 27096, 53872, 24632, 52225], 'chosen_samples_score': ['1.01094', '1.0118093', '1.0136554', '1.0179014', '1.0833211', '1.0681667', '1.0346322', '1.163536', '1.161297', '1.2553414']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9329, 'nll': 0.5018532410264015}, 'chosen_samples': [53068, 18525, 1674, 1423, 42746, 8771, 17485, 31806, 8459, 16628], 'chosen_samples_score': ['1.046395', '1.0564644', '1.0658973', '1.1285021', '1.0783885', '1.1611627', '1.0909724', '1.1274946', '1.1212186', '1.0815346']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9237, 'nll': 0.553655019402504}, 'chosen_samples': [51764, 50826, 53316, 51464, 34328, 35694, 40576, 5720, 54601, 32427], 'chosen_samples_score': ['1.0100449', '1.0106941', '1.0517337', '1.0212153', '1.1609876', '1.023237', '1.0330925', '1.054364', '1.021457', '1.0129831']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.933, 'nll': 0.497387558221817}, 'chosen_samples': [12826, 30468, 27429, 44736, 50233, 1239, 55278, 53976, 50546, 40466], 'chosen_samples_score': ['0.9425546', '0.9542452', '0.9546042', '0.98438925', '0.9932861', '0.99878633', '1.0055517', '1.0744827', '1.0090997', '1.089741']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9372, 'nll': 0.468498632311821}, 'chosen_samples': [3251, 16834, 3672, 20169, 10565, 11616, 31252, 20037, 57507, 49500], 'chosen_samples_score': ['0.9790509', '0.9818225', '1.0004127', '1.2219703', '1.0442283', '1.0266886', '1.0127877', '0.99213535', '0.9822611', '1.0620521']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9412, 'nll': 0.4688630163669586}, 'chosen_samples': [22095, 26756, 45422, 29286, 28362, 52169, 51863, 10210, 16039, 37773], 'chosen_samples_score': ['1.0498283', '1.0538464', '1.0688529', '1.0697055', '1.1282725', '1.0880392', '1.0798537', '1.1167238', '1.0766236', '1.1617169']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9421, 'nll': 0.45875823497772217}, 'chosen_samples': [18220, 29711, 54802, 24609, 52516, 37078, 54960, 21601, 24479, 50556], 'chosen_samples_score': ['1.0376439', '1.0394087', '1.0460179', '1.0501971', '1.055869', '1.093744', '1.0614138', '1.1013896', '1.1261117', '1.0946145']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9398, 'nll': 0.42409465163946153}, 'chosen_samples': [18150, 58249, 1573, 23629, 56379, 33224, 52089, 44123, 59297, 26760], 'chosen_samples_score': ['0.9575419', '0.9610661', '0.9710608', '0.9772138', '0.993826', '0.97631323', '1.0031497', '1.0160072', '0.97220045', '1.0061064']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.95, 'nll': 0.3948081538081169}, 'chosen_samples': [7168, 47597, 47741, 39116, 22633, 54892, 12305, 13388, 26358, 57742], 'chosen_samples_score': ['0.9761514', '0.98070675', '0.98168975', '0.98388207', '0.98761874', '1.02931', '0.99363697', '1.0584059', '1.0544903', '1.0069025']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9505, 'nll': 0.3904214143753052}, 'chosen_samples': [38165, 34829, 18487, 30724, 52582, 59280, 43176, 4332, 9180, 15975], 'chosen_samples_score': ['1.0074279', '1.0900844', '1.0456403', '1.1509087', '1.009752', '1.0613995', '1.07808', '1.0820837', '1.1061803', '1.016156']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9521, 'nll': 0.36484903544187547}, 'chosen_samples': [45602, 31706, 15191, 52674, 12663, 37373, 13428, 22139, 17079, 36744], 'chosen_samples_score': ['0.95629346', '0.9715345', '0.9760847', '1.0005755', '1.0216277', '0.97451013', '0.99854016', '0.9891908', '1.0854578', '0.9755197']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9573, 'nll': 0.3836706355214119}, 'chosen_samples': [33505, 53570, 32280, 5163, 5679, 16836, 52138, 44328, 26482, 59335], 'chosen_samples_score': ['1.0549413', '1.0735824', '1.0760965', '1.0810542', '1.1001325', '1.0890384', '1.08483', '1.0972631', '1.1074878', '1.0855622']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9537, 'nll': 0.39749342799186704}, 'chosen_samples': [1075, 34946, 39309, 8765, 59390, 28844, 41349, 46379, 37469, 17265], 'chosen_samples_score': ['1.0220033', '1.0233109', '1.0260065', '1.0944517', '1.1014557', '1.1168978', '1.0788823', '1.0823603', '1.0709443', '1.1062928']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9569, 'nll': 0.3806169882416725}, 'chosen_samples': [48521, 52838, 49012, 38698, 13942, 47549, 44157, 28536, 44172, 6291], 'chosen_samples_score': ['1.0687872', '1.089266', '1.0911119', '1.1011018', '1.2577033', '1.2381788', '1.1376214', '1.1346123', '1.1702778', '1.13145']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9559, 'nll': 0.40552629381418226}, 'chosen_samples': [57972, 24728, 4638, 31301, 52757, 3810, 14878, 59720, 10417, 13998], 'chosen_samples_score': ['1.0775268', '1.0941925', '1.0947697', '1.1025527', '1.261087', '1.1093665', '1.1284162', '1.1328776', '1.1030272', '1.1692154']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9595, 'nll': 0.352000392973423}, 'chosen_samples': [58874, 52971, 29827, 30900, 12594, 5129, 8730, 27540, 32747, 40654], 'chosen_samples_score': ['0.9655666', '0.9771827', '1.0248508', '1.1038256', '1.0473044', '1.0183527', '1.0061221', '1.0991738', '1.0523031', '1.0213633']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9541, 'nll': 0.3483010709285736}, 'chosen_samples': [48360, 70, 15948, 15771, 18501, 15510, 42703, 48912, 22083, 13969], 'chosen_samples_score': ['0.90246314', '0.90445834', '0.91842586', '0.9253739', '0.961761', '0.93746704', '1.1124429', '0.9432427', '1.0174434', '0.97563237']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9609, 'nll': 0.38057036995887755}, 'chosen_samples': [50572, 52086, 47926, 38280, 54858, 8680, 14357, 28654, 59747, 49889], 'chosen_samples_score': ['0.9858275', '0.9908512', '0.992818', '1.0181024', '0.99357915', '1.0059779', '1.0098069', '1.0972435', '0.9939142', '1.002117']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9662, 'nll': 0.33599049001932146}, 'chosen_samples': [32880, 18003, 37392, 1015, 14692, 44753, 57667, 20280, 3367, 32776], 'chosen_samples_score': ['1.027335', '1.0326877', '1.0331558', '1.0372456', '1.0944157', '1.0818012', '1.1616021', '1.0781611', '1.0386734', '1.1621062']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.962, 'nll': 0.3440460726618767}, 'chosen_samples': [21608, 57665, 47387, 27739, 49200, 41540, 32738, 5315, 38050, 15579], 'chosen_samples_score': ['0.9011302', '0.9057186', '0.90341455', '0.90326756', '0.90925515', '0.9181109', '0.965385', '0.93659425', '0.929373', '0.9329711']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9606, 'nll': 0.34881808012723925}, 'chosen_samples': [52462, 50351, 43618, 43560, 49563, 5103, 9547, 39561, 24587, 9118], 'chosen_samples_score': ['0.95669425', '0.9635117', '0.97948325', '1.0130224', '1.0049736', '0.98387456', '0.98244905', '1.0532504', '0.9823716', '1.1091988']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9625, 'nll': 0.34816415160894393}, 'chosen_samples': [42844, 36072, 43745, 21700, 6428, 19344, 27085, 59339, 11292, 45026], 'chosen_samples_score': ['0.97092533', '0.9730392', '1.0134671', '0.97838753', '1.1424398', '1.1058729', '0.98206085', '0.98623854', '1.0489862', '1.0277035']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9663, 'nll': 0.31288113445043564}, 'chosen_samples': [41453, 16959, 31738, 59294, 47403, 41464, 16572, 20171, 37048, 57718], 'chosen_samples_score': ['0.94651294', '0.97108936', '0.9779195', '0.9725635', '0.986065', '0.99515104', '0.99545133', '1.0024503', '1.0195599', '1.0173836']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9656, 'nll': 0.31195101886987686}, 'chosen_samples': [42973, 53873, 1376, 27172, 14201, 22824, 21896, 37750, 394, 14602], 'chosen_samples_score': ['0.9043099', '0.9113079', '0.9135147', '0.91783625', '0.9508487', '0.96190304', '0.9843337', '0.92346907', '0.9653041', '0.92726964']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.967, 'nll': 0.33679224699735644}, 'chosen_samples': [40530, 19501, 17585, 36439, 2839, 40066, 57728, 34942, 5042, 52218], 'chosen_samples_score': ['0.9871943', '0.99294937', '1.1886454', '1.1004472', '1.1293651', '1.0720503', '1.0263331', '1.0085146', '1.0176661', '1.0094712']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9705, 'nll': 0.3066131055355072}, 'chosen_samples': [45443, 57768, 27576, 17209, 35324, 33340, 6980, 55906, 27176, 20641], 'chosen_samples_score': ['1.0166821', '1.0201712', '1.0293012', '1.1261785', '1.0252353', '1.083255', '1.031756', '1.0389782', '1.0335909', '1.0548499']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9677, 'nll': 0.329223045706749}, 'chosen_samples': [27793, 15402, 29723, 3370, 40158, 42085, 37450, 47220, 29179, 14896], 'chosen_samples_score': ['0.98092705', '0.98842883', '0.9887378', '0.9906301', '0.9996331', '1.0005149', '1.0182097', '1.0338597', '1.0366197', '1.1620541']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9696, 'nll': 0.29076784253120425}, 'chosen_samples': [19540, 58832, 37161, 35406, 14765, 28491, 12277, 15276, 46435, 9147], 'chosen_samples_score': ['0.8957763', '0.97595793', '0.9299706', '0.9374229', '1.0815096', '1.0111682', '0.92658055', '0.90640336', '0.9571757', '0.9080334']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9653, 'nll': 0.32247920483350756}, 'chosen_samples': [17540, 8417, 44234, 4153, 39320, 25994, 49910, 50930, 5216, 55739], 'chosen_samples_score': ['0.8453089', '0.84600514', '0.86279595', '0.8779423', '0.98334694', '0.867231', '0.8671802', '0.9153082', '0.8497138', '0.85257804']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9685, 'nll': 0.29763668328523635}, 'chosen_samples': [13981, 45944, 10044, 51394, 34520, 18324, 31512, 15803, 47036, 56173], 'chosen_samples_score': ['0.99597305', '1.0054694', '1.0261544', '1.0303636', '1.0166339', '1.0552926', '1.1581534', '1.0863307', '1.0946865', '1.116492']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9708, 'nll': 0.3123889520764351}, 'chosen_samples': [14246, 21636, 20784, 28368, 10500, 7308, 46268, 29672, 2872, 23788], 'chosen_samples_score': ['0.94461983', '0.95280445', '0.9555247', '0.96031314', '1.1803102', '0.9811853', '1.0458941', '0.9910828', '1.010803', '0.9796144']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9725, 'nll': 0.2567608192563057}, 'chosen_samples': [46288, 36818, 21880, 11708, 44534, 55792, 14062, 34847, 30952, 22607], 'chosen_samples_score': ['0.94252807', '0.9485398', '0.9445316', '0.95078486', '0.9679804', '1.0380934', '0.96240866', '1.0493829', '0.96537876', '0.98024297']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9664, 'nll': 0.30768041461706164}, 'chosen_samples': [41850, 55194, 25909, 15141, 38389, 3798, 5295, 39208, 31345, 49573], 'chosen_samples_score': ['0.93004155', '0.9351007', '0.94471717', '0.95486116', '0.9553683', '0.96768135', '1.0219495', '1.0820707', '0.97665685', '1.0251365']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9712, 'nll': 0.29335263967514036}, 'chosen_samples': [37160, 7768, 11074, 54885, 19025, 48997, 54954, 35017, 37800, 1518], 'chosen_samples_score': ['0.8992026', '0.9131583', '0.9624855', '0.9281966', '0.9199566', '1.0732378', '0.92061543', '0.92368704', '0.95084274', '1.0079671']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9699, 'nll': 0.29151835292577744}, 'chosen_samples': [5298, 8228, 31883, 29294, 7259, 3136, 39423, 47443, 50982, 7207], 'chosen_samples_score': ['0.9814826', '1.0141113', '0.9832271', '0.9824169', '1.0269952', '1.0207114', '1.0112851', '1.0961347', '1.0493855', '1.0080817']})
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store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9824, 'nll': 0.19337458163499832}, 'chosen_samples': [1930, 22481, 40976, 11534, 55190, 36714, 55206, 27556, 41299, 33162], 'chosen_samples_score': ['0.82750404', '0.83383054', '0.84208804', '0.9335275', '0.9252659', '0.843675', '0.87104607', '0.8493881', '0.8691872', '0.85478777']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9845, 'nll': 0.19337892532348633}, 'chosen_samples': [40755, 4955, 9431, 10736, 15434, 36982, 18302, 43212, 36408, 26722], 'chosen_samples_score': ['0.7839225', '0.79125035', '0.8000438', '0.80191064', '1.0293555', '0.8264898', '0.89608616', '0.81593406', '0.83468103', '0.87255496']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.982, 'nll': 0.19047757238149643}, 'chosen_samples': [54966, 29360, 50714, 42828, 42866, 29821, 24860, 6269, 42526, 40046], 'chosen_samples_score': ['0.8032361', '0.8106036', '0.8149918', '0.81651044', '0.84952426', '0.8528016', '0.8297842', '0.8562475', '0.8695841', '0.9815916']})
store['iterations'].append({'num_epochs': 27, 'test_metrics': {'accuracy': 0.9842, 'nll': 0.1935315504670143}, 'chosen_samples': [39877, 29226, 47247, 15912, 6387, 43310, 30521, 14697, 32499, 53264], 'chosen_samples_score': ['0.920788', '0.9323195', '0.939876', '0.9955874', '0.9629626', '0.9442701', '0.95620036', '0.94060355', '0.9951962', '0.9992931']})
